{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:04:30Z","timestamp":1777043070556,"version":"3.51.4"},"reference-count":13,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,3,13]]},"DOI":"10.1117\/12.2254195","type":"proceedings-article","created":{"date-parts":[[2017,3,13]],"date-time":"2017-03-13T15:50:06Z","timestamp":1489420206000},"page":"1013710","source":"Crossref","is-referenced-by-count":188,"title":["Deep learning for brain tumor classification"],"prefix":"10.1117","volume":"10137","author":[{"given":"Justin S.","family":"Paul","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ. (United States)"}]},{"given":"Andrew J.","family":"Plassard","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ. (United States)"}]},{"given":"Bennett A.","family":"Landman","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ. (United States)"}]},{"given":"Daniel","family":"Fabbri","sequence":"additional","affiliation":[{"name":"Vanderbilt Univ. (United States)"}]}],"member":"189","reference":[{"key":"c1","article-title":"Correction: Enhanced performance of brain tumor classification via tumor region augmentation and partition","volume":"10","author":"Cheng","year":"2015"},{"key":"c2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"c3","first-page":"2278","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998"},{"key":"c4","doi-asserted-by":"publisher","DOI":"10.1093\/mnras\/stv632"},{"key":"c5","article-title":"Toward content based image retrieval with deep convolutional neural networks","volume":"9417","author":"Sklan","year":"2015"},{"key":"c6","doi-asserted-by":"publisher","DOI":"10.1371\/annotation\/0c88e0d5-dade-4376-8ee1-49ed4ff238e2"},{"key":"c7","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014"},{"key":"c8","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"c9","first-page":"1319","article-title":"Maxout networks","volume":"28","author":"Goodfellow","year":"2013"},{"key":"c10","first-page":"275","article-title":"Deep sparse rectifier neural networks","volume":"15","author":"Glorot","year":"2011"},{"key":"c11","first-page":"372","article-title":"A method of solving a convex programming problem with convergence rate o(1\/sqr(k))","volume":"27","author":"Nesterov","year":"1983"},{"key":"c12","unstructured":"Lasagne Contributors, \u201cLasagne (0.2.dev1).\u201d http:\/\/lasagne.readthedocs.io\/ (2016)."},{"key":"c13","unstructured":"Theano Development Team, \u201cTheano: A python framework for fast computation of mathematical expressions,\u201d arXiv 1605 (2016)."}],"event":{"name":"SPIE Medical Imaging","location":"Orlando, Florida, United States"},"container-title":["SPIE Proceedings","Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging"],"original-title":[],"deposited":{"date-parts":[[2018,9,27]],"date-time":"2018-09-27T01:06:16Z","timestamp":1538010376000},"score":1,"resource":{"primary":{"URL":"http:\/\/proceedings.spiedigitallibrary.org\/proceeding.aspx?doi=10.1117\/12.2254195"}},"subtitle":[],"editor":[{"given":"Andrzej","family":"Krol","sequence":"first","affiliation":[{"name":"SUNY Upstate Medical Univ. (United States)"}]},{"given":"Barjor","family":"Gimi","sequence":"additional","affiliation":[{"name":"Geisel School of Medicine at Dartmouth (United States)"}]}],"short-title":[],"issued":{"date-parts":[[2017,3,13]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1117\/12.2254195","relation":{},"ISSN":["0277-786X"],"issn-type":[{"value":"0277-786X","type":"print"}],"subject":[],"published":{"date-parts":[[2017,3,13]]}}}